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Hanashi
201 lines
12 KiB
Markdown
201 lines
12 KiB
Markdown
# MiniMax H3 — measured benchmarks
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Measured 2026-08-06 with `bench/minimax_bench.py` through the ComfyUI API.
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Raw data: `bench/results.jsonl` (B200), `bench/4090/`, `bench/4000/`.
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Regenerate the tables with `python3 bench/summarize.py`.
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**Method.** ComfyUI 0.30 headless; template-reference sampling everywhere:
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`res_multistep` sampler, `simple` scheduler, **20 steps**, no CFG, 24 fps,
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`nvfp4_awq` Qwen3-VL-32B text encoder, fp16 video VAE + fp32 audio VAE.
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Times are ComfyUI execution time (`execution_start`→`execution_success`), which
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excludes client/queue overhead (wall time tracked separately in the raw data;
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typically +1–2 s). "Warm" = model already resident; first-load runs are
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recorded but excluded from the means. Repeated configs use distinct seeds so
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ComfyUI's node cache can't short-circuit. Reference inputs are real images
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(film stills, anime characters, forests/volcanos, up to 23040×3840 —
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`scripts/pod/fetch-bench-images-v2.py`).
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Hardware: **B200** 180 GB (US, $6.94/hr) · **RTX 4090** 24 GB (EU, $0.74/hr) ·
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**RTX PRO 4000 Blackwell** 24 GB (EU, $0.57/hr). The EU cards run pruned int8
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only (bf16/int8 don't fit 24 GB usefully) with `--reserve-vram 2.5`; the RTX
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4000 additionally needs `--cache-none --disable-pinned-memory` because its
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container has a 29 GB RAM cap that otherwise OOM-kills weight staging.
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## B200: weight-family comparison (warm, 20 steps)
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The headline: **bf16 is the fastest family on the B200** — quantization costs
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speed here (dequant overhead) and only pays when VRAM is scarce.
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| Config | bf16 | int8 | pruned int8 | bf16 VRAM | int8 VRAM | pruned VRAM |
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|---------------------------------|-------------|-------------|-------------|-----------|-----------|-------------|
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| t2v 864×480 5 s | **51 s** | 63 s | 63 s | 159 GB | 63 GB | 69 GB |
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| t2v 864×480 15 s | **240 s** | 270 s | 270 s | 182 GB | 101 GB | 75 GB |
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| t2v 1344×768 5 s | **169 s** | 197 s | 194 s | 177 GB | 99 GB | 73 GB |
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| ref2v 1 ref 864×480 5 s | **54 s** | 68 s | ~68 s¹ | 186 GB | 95 GB | 68 GB |
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| ref2v 4 refs match | **63 s** | 86 s | 81 s | 154 GB | 95 GB | 70 GB |
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| ref2v 8 refs match | **79 s** | 99 s | 95 s | 185 GB | 96 GB | 68 GB |
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| ref2v 8 refs match 1344×768 | **282 s** | 335 s | 339 s | 158 GB | 103 GB | 77 GB |
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| ref2v 4 refs max | **91 s** | 117 s | 112 s | 156 GB | 96 GB | 70 GB |
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| ref2v 8 refs max | **785 s** | 891 s | 841 s | 177 GB | 112 GB | 92 GB |
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¹ pruned 1-ref mean is 90±41 s over 3 runs because the first run absorbed a
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model swap; the steady-state runs are ~68 s, matching int8.
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bf16 peaks at 155–187 GB — it *only* runs on ≥180 GB cards. int8 fits under
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112 GB (H200-class); pruned int8 under ~95 GB worst-case, ~70 GB typical
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(H100-class without offload, 24 GB cards with offload).
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## Scaling behavior (B200, pruned int8)
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Duration (864×480 t2v): 5 s → 63 s, 10 s → 154 s, 15 s → 270 s. Cost per
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output-second rises from 12.6→15.4→18.0 s — mildly superlinear (attention).
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Resolution (5 s t2v): 608×352 → 32 s, 864×480 → 63 s, 1344×768 → 194 s.
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Roughly ∝ pixels^1.2. The corner case 1344×768×15 s = **1108 s** (18.5 min).
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Reference count (864×480 5 s, `match` sizing): 1 ref ≈ 68 s, 4 refs ≈ 81 s,
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8 refs ≈ 95 s — ~+4 s per extra reference image. Cheap.
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Reference sizing `max` is the expensive lever: with 8 large refs it goes
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95 s → **841 s** (~9×), because 2048px reference tokens ride through every
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sampling step. Use `match` unless identity fidelity demands otherwise.
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## Cross-GPU (pruned int8, warm, 20 steps — final measured)
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t2v 864×480 5 s:
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| GPU | $/hr | time/video | videos/hr | $/video | notes |
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|------------------|-------|------------------|-----------|---------|------------------------------------|
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| B200 (bf16) | $6.94 | 51 s (n=2) | 70 | $0.098 | fastest family on this card |
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| B200 (pruned) | $6.94 | 63 s (n=3) | 57 | $0.121 | |
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| RTX 4090 | $0.74 | 222 s (n=3, ±0) | 16 | $0.046 | --reserve-vram 2.5 |
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| RTX PRO 4000 | $0.57 | 553 s (n=3, ±3) | 6.5 | $0.087 | --cache-none tax (29 GB RAM cap) |
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ref2v single ref, 864×480 5 s:
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| GPU | time/video | $/video |
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|------------------|------------------|---------|
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| B200 (bf16) | 54 s (n=3) | $0.104 |
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| RTX 4090 | 287–392 s | $0.059–0.081 |
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| RTX PRO 4000 | 581 s (n=3, ±6) | $0.092 |
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**RTX 5090 (32 GB, $0.99/hr) — added later, the fleet workhorse:**
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| Config (pruned int8, warm) | time | $/video | notes |
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|-----------------------------|------------------|---------|------------------------------------|
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| t2v 864×480 5 s | 143 s (n=3, ±1) | $0.039 | cheapest t2v measured |
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| ref2v 1 ref | 153 s (n=3, ±0) | $0.042 | |
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| ref2v 8 refs full-size | 220 s (n=3, ±1) | $0.060 | vs $0.152 B200-bf16: 2.5× cheaper |
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| ref2v 9 refs full-size | 237 s | $0.065 | max ref count |
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| t2v 15 s | 582 s | $0.160 | works (killed 24 GB pre-patch) |
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| t2v 1344×768 | 455 s | $0.125 | works |
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| 9:16 480×864 / 1:1 640×640 | 187 s / 141 s | — | aspects fine |
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| ref2v 9 refs `max` sizing | 2031 s (34 min) | $0.559 | works; avoid in production |
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**bf16 on the 5090** (66 GB DiT layer-streamed through 32 GB): warm t2v =
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**110 s — faster than its own pruned int8 (143 s)**; ref2v 1/4/8 refs =
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310/336/369 s (needs `--cache-none` when switching between bf16 models —
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two 66 GB stacks break the 124 GB container RAM cap). B200-quality output
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at $0.99/hr for everything except 15 s + full-res extremes.
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**Verdict:** the 5090 dominates cost AND capability among consumer cards —
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cheapest per video on every task shape, full reference envelope, and a
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credible bf16 quality tier. The patched 4090 (see MITIGATIONS.md) is a
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viable budget multi-ref tier (~8-9 refs at 429–444 s). The RTX PRO 4000's
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low price is consumed by its slow, reload-heavy runs — dropped. Fleet
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recipe: 5090 pools for volume, B200 pool only for 15 s/full-res premium
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jobs and fastest-latency needs.
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## Concurrency (2 ComfyUI instances, one B200)
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See `docs/CONCURRENCY.md` for the model. Experiment: two resident instances,
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sequential pair vs concurrent pair of identical warm t2v jobs.
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Measured (pruned int8, t2v 864×480 5 s, both instances warm):
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| Mode | Total for 2 videos | Per-video effective |
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|-------------------------------|--------------------|---------------------|
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| Sequential (one instance) | **128 s** | 64 s |
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| Concurrent (two instances) | **145 s** | 72.5 s |
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Concurrent was **13% slower** for the same work — each co-scheduled job ran
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142 s vs 63 s alone (2.27×). There is no pipeline-overlap win; cache and
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scheduler contention make co-scheduling strictly worse. **One worker per GPU,
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queue in front, scale horizontally.** (This also removes any argument for
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MPS/MIG partitioning for throughput.)
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## Reliability & failure statistics
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Every run's end state and error is recorded in the JSONL files; regenerate the
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failure tables with `python3 bench/failures.py`, and validate output files
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with `python3 bench/validate_outputs.py` (downloads each MP4 and ffprobes
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streams + duration). Snapshot as of 2026-08-06 ~09:00 UTC:
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| GPU | success rate | dominant failures |
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|-------------------|--------------|-------------------------------------------------------|
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| B200 180 GB | 96/96 (100%) | none |
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| RTX 4090 24 GB | 9/53 (17%) | VRAM OOM: TE vision (multi-ref), DiT sampling (refs/15s/full-res); cascading server death |
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| RTX PRO 4000 24 GB| 7/12 (58%) | container-RAM OOM (pre-flags); corrupt ref image (fixed); server death on 15 s t2v |
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**Output validation**: 100% of successful runs produced valid videos (h264 +
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stereo AAC at exactly the requested duration) — every success on all three
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GPUs checks out. (Three early B200 outputs 404 on re-download because they
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were written to the container disk that the pod reset wiped — outputs now go
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to the persistent volume via `--output-directory`.)
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**The 24 GB failure pattern is reference images, not duration/resolution.**
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t2v succeeds reliably on both consumer cards at every size tried; ref2v OOMs
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in `torch._int_mm` during Qwen3-VL image encoding: the 15 GB text encoder plus
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multi-image vision activations exceeds 24 GB (observed 22.1 GB peak at OOM).
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With aggressive flags (`--cache-none --disable-pinned-memory --reserve-vram
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2.5`) a **single** reference works (4090: 392 s); 4-ref and 8-ref
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configurations fail consistently, and repeated OOMs eventually killed the
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server process, cascading failures across the rest of that suite (harness now
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survives this and records `server_died`).
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A second environment trap on cheap tiers: the RTX 4000 pod's container has a
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**29 GB RAM cap** that OOM-killed weight staging until `--cache-none
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--disable-pinned-memory` was applied; both EU pods also shipped torch 2.4.1,
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too old for H3 (needs ≥2.5; Blackwell needs ≥2.7+cu128).
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**Fleet implication:** consumer 24 GB cards are viable for t2v/i2v and
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single-ref ref2v only. Multi-reference work — the priority modality — needs
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≥32 GB (5090-class, unverified) and realistically ≥48 GB for headroom, or it
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stays on big-GPU pools.
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## Multi-ref rescue status (in progress)
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Community research (see agent report summarized in commit history) identified
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the root cause as **ComfyUI PR #15316**: VRAM is budgeted for TE weights but
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not image-encode activations. Two OOM phases were isolated on the 4090:
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1. **TE vision encode** — fixed by ref pre-downscaling (`--ref-downscale`,
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now in the harness) and/or the #15316 patch + higher `--reserve-vram`.
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Confirmed: with 0.1–0.2 MP refs the TE phase completes.
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2. **DiT sampling with ref tokens** — the 20 GB DiT + ref-token-enlarged
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attention doesn't fit 24 GB. `--lowvram` made things worse (crashes).
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Remaining candidates: #15316 patch active + `--reserve-vram 5` (patched
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file is applied on both EU pods, pending verification), TE eviction via
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ComfyUI-H3-Multishot, or a smaller DiT quant (community INT4/W4A8,
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~10–12 GB, quality TBD).
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Verification was interrupted when all three pods went offline simultaneously
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(apparent RunPod account event ~09:30 UTC). Resume plan lives in the repo:
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restart pods → `scripts/local/push-to-pod.sh` → rescue matrix via
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`bench/minimax_bench.py --suite refheavy --ref-downscale 0.2`.
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## Takeaways
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1. **On the B200, run bf16.** Fastest and highest quality; quantized families
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exist for smaller cards, not for speed.
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2. **Many references are cheap; big references are not.** 8 refs at `match`
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costs ~1.4× a single ref. `max` sizing costs up to ~9×. Default `match`.
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3. **Duration and resolution both scale superlinearly**; the trained envelope
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corner (15 s @ 1344×768) costs ~18 min even on a B200 — treat full-res
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long clips as premium jobs.
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4. **24 GB consumer cards work but pay a heavy offload tax** (~3.5× B200
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latency rather than the ~3× raw-compute ratio), and cheap pod tiers bring
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operational traps: container RAM caps (29 GB on the RTX 4000 pod) and
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stock torch builds too old for H3 (needs ≥2.5 for `enable_gqa`; Blackwell
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needs ≥2.7+cu128).
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